New Labs - Malterminal: Malware Analysis
With artificial intelligence (AI) and large language models (LLMs) fast becoming a more popular and talked-about set of technologies in every industry in society, it's no surprise that LLM-enabled malware now exists that can dynamically generate code, query data, and offload malicious functionality to LLMs, lowering the barrier of entry for threat actors deploying malware. This lab introduces one of the first known malware samples to ever facilitate the use of LLMs to perform malicious functionality. Why should our customers care? Most, if not all, companies are looking into using AI to varying degrees, whether to make their workforce more efficient and productive or to build full models that facilitate technical processes. With this in mind, and with the advent of basic malware that can use API keys to query LLMs and AI services, we will likely see this particular malware set evolve over time. By doing this lab, you'll begin to see how these pieces of malware are just the stub and querier for AI and how they can be used maliciously. This will showcase what this threat is like in its current state. We shall be monitoring how this threat evolves, so stay tuned for more labs. Who is the defensive lab for? SOC Analysts Incident Responders Threat Hunting Here is a link to the lab: https://immersivelabs.online/v2/labs/malterminal-analysisUnlock the World of AI: Introducing Our New AI Foundations Collection!
That's why we’re thrilled to announce the launch of our new AI Foundations collection, a comprehensive set of labs designed to empower you to navigate the fast-paced world of AI confidently. This seven-part lab collection is your guided tour through the core components of modern AI implementation. We've crafted this collection for everyone, breaking down complex concepts into digestible, easy-to-understand labs. Whether you're a seasoned tech professional or just starting your AI journey, this collection will provide you with a practical, hands-on understanding of how AI systems are built and how they work together to deliver powerful capabilities. NOTE: These labs are only available for customers who haven’t opted out of AI-related content. Why a new AI collection? Our customers have asked for more in-depth AI content – a demand that mirrors the explosive growth of the AI market. This new collection is our commitment to staying at the forefront of the industry and proactively addressing the needs of our community. What you'll learn The AI Foundations collection is a journey through the essential concepts of artificial intelligence. Each lab builds on the last, culminating in a holistic understanding of modern AI systems, with a special focus on agentic AI. Here's a glimpse into what you'll discover: Episode 1: Artificial Intelligence (Theory): Dive into the fundamentals of AI, exploring what it is, how it works, and the distinctions between generative AI and AGI. It also discusses AI’s limitations and demystifies the "illusion of thinking". Episode 2: Core Components (Theory): Get acquainted with the building blocks of AI, including LLMs, embedding and diffusion models, RAG, MCP, and the exciting world of agentic AI. It also touches on crucial security considerations as AI transitions from "knowing" to "doing". Episode 3: Large Language Models (Theory): Explore the power of foundational models, the importance of fine-tuning, the role of system prompts, and security considerations such as exploitable vulnerabilities and data privacy. Episode 4: Retrieval Augmented Generation (RAG) (Practical): Take a deep dive into RAG, vector databases, embedding, and chunking. In this hands-on lab, you'll create a knowledge base, chunk a file, and query a fictional company's proprietary data through an integrated AI chatbot. Episode 5: Model Context Protocol (MCP) (Practical): Understand the MCP protocol and its architecture within the broader AI landscape. You'll get hands-on experience using MCP Inspector to interact with an MCP server, and instruct an AI chatbot to organize files on your desktop, gaining insight into exactly how tools are chosen and invoked. Episode 6: Agentic AI (Practical): Immerse yourself in the world of AI agents. You'll get access to real AI agents within a safe sandbox environment. The curious can poke and edit the code and explore integrated Langfuse for a deeper look into the observability of the AI system. Episode 7: Demonstrate Your Knowledge (Theory): Put your newfound knowledge to the test and solidify your understanding of the concepts covered throughout the collection. Secure and private by design We've built our practical AI labs with your security and privacy as the top priority. When you launch a lab, you're entering a completely isolated, sandboxed environment. These sandboxes are self-contained and have no connection to any customer data or personal information. Think of it as your own private, temporary workspace that’s thoroughly purged after each use. To interact with the AI models, each lab session creates temporary user credentials. Not only are these credentials temporary, but they’re also locked to the lab environment itself. This means that even if the credentials were to be exposed, they would be useless outside of the specific lab they were created for, providing a robust layer of security. Access to the internet is also strictly controlled, which only allows connections to the minimum endpoints required for the lab to function. We utilize privacy-centric AI models designed to protect your data. The models we use don’t store or log your prompts and completions. Furthermore, your interactions are never used to train any models, ensuring that your data remains your own. We’ve also opted out of any content being used for service improvements across all the AI services we use. In some of our more advanced labs, we've implemented an additional layer of security with guardrails that preprocess user inputs and model outputs to filter for harmful or inappropriate content. These guardrails are mandatory and can’t be bypassed by users within the lab environment. These multiple layers of security work together to provide a safe and secure environment for you to learn and experiment with AI. Who is this collection for? Everyone! We've designed these labs to be a guided walkthrough, making even the more technical details accessible to anyone working with or interested in AI. Whether you're a developer, a business leader, a student, or simply a curious mind, our AI Foundations course will equip you with the knowledge and skills to thrive in the age of artificial intelligence. Join us on this exciting journey and unlock the power of AI. Get ready to build, innovate, and lead in the new era of intelligence.New Labs: BlackHat 2025 and DefCon 33
Throughout early August 2025, representatives from Immersive's cyber team attended the BlackHat 2025 and DefCon 33 conferences and got great exposure to the latest technologies, topics, and techniques presented by the sharpest minds in our industry. As a result of attending these talks, workshops, and villages, Immersive has created brand new labs going through the various talks that took place, allowing you to get hands-on with the latest technologies and exploits. We present a number of brand new labs covering some of the most interesting and insightful topics from the events, from operational technology (OT) to achieving privilege escalation through firewall software. AI was a hot topic, as you would imagine, especially around Prompt Injection attacks. We already have plenty of content on Prompt Injection, not to mention the new AI Foundations content, so for this series, we created an Appsec Style lab around preventing Prompt Injection attacks. Why should our customers care? BlackHat and DefCon are two conferences that attract the greatest minds in cyber to get together and share their knowledge through workshops, official talks, and villages. Given the high diversity of events and talks that took place, there is something for everyone! Many of the topic areas shared are things that attackers could easily exploit themselves, so taking advantage of the information in these labs equips our customers with the knowledge of the latest vulnerabilities, threats, and exploitation techniques currently being talked about in the industry - improving your resilience and preparation against the latest threats. Who are the labs for? Offensive Security Engineers and Penetration Testers SOC Analysts and Incident Responders Malware Reverse Engineers Operational Technology Engineers Cyber Security Engineers Here is a list of the labs in this release: Binary Facades: Extracting Embedded Scripts CVE-2024-5921 Redux - Bypassing mitigations to PrivEsc with Palo Alto Global Protect Chrome Alone: Transforming a Browser into a C2 Platform No VPN Needed?: Cryptographic Attacks Against the OPC UA Protocol Python: AI Prompt Injection If you'd like to do any of these labs, here is a link to the BlackHat/DefCon collection: https://immersivelabs.online/series/defcon-black-hat/Artificial Intelligence: Navigating the Evolving Landscape
The changing world To understand where we're going, you first need to grasp the sheer scale of what's happening now. The May 2025 report on Artificial Intelligence Trends by Mary Meeker and Bond Capital paints a vivid picture of a sector in overdrive: Unprecedented user adoption: Generative AI tools have achieved mass adoption faster than any previous technology, including the internet and smartphones. Soaring infrastructure investment: Top tech giants (Apple, NVIDIA, Microsoft, Alphabet, Amazon, Meta) spent a combined $212 billion on capital expenditures in 2024, a huge portion of which was dedicated to AI infrastructure like data centres and custom silicon. Shifting cost dynamics: The cost to train a state-of-the-art foundation model remains astronomically high, somewhere in the hundreds of millions of dollars. However, the cost to use these models (the inference cost) is plummeting, making AI more accessible than ever before. Intense competition and rapid imitation: AI is boosting productivity and driving competition between products. Global AI "space race": Nations are treating AI supremacy as a strategic imperative, leading to significant government investment and policy-making, particularly in areas like the semiconductor supply chain, with the US, Europe, and China all building new fabrication plants. With this level of investment and adoption, can you confidently say this is a bubble about to burst? Sir Demis Hassabis, CEO of Google DeepMind, puts this huge change on the same magnitude as the industrial revolution and the launch of the internet. Data from Gartner supports this, suggesting that by the end of 2025, 39% of organizations worldwide will have moved into the experimentation phase of AI adoption. The shift is well and truly on. What does AI look like in 2025? AI is underpinned by machine learning models, which are trained, not programmed. Engineers feed them vast amounts of data, and they learn patterns, concepts, and relationships. Different types of models are used for different purposes, such as those specialising in human language interactions (large language models, LLMs) and artwork generation (diffusion models). When using AI systems, such as chatbots, you’re not interacting with the model directly but rather with additional software that uses the model as its “brain”. This allows you to implement guardrails to check user inputs and model outputs, helping to filter out harmful or inappropriate content. Modern AI systems are rarely just a wrapper around a model. They integrate with other tools and services to enhance their capabilities, such as searching the web for real-time information or accessing private company documents to provide context-specific answers. The year of agentic AI An AI agent is a system that can autonomously pursue a goal. Instead of responding to a single prompt, it can reason, plan, and execute a series of steps to accomplish a complex task. It can also decide which tools to use and in what order. An AI agent may still be a chatbot or run constantly in the background. Big tech companies are adamant that agentic AI is the next evolution, with Google, Amazon, and Microsoft all predicting the next wave of innovation over the next two years. A key catalyst for this explosion was the release of the open-source Model Context Protocol (MCP) by Anthropic in late 2024. MCP provides a standardized way for AI models to discover and use tools. As the official documentation puts it: "Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals... MCP provides a standardized way to connect AI models to different data sources and tools." Source: Model Context Protocol - Getting Started MCP has been a game-changer, dramatically simplifying the process of giving AI systems new capabilities and accelerating the move from AI systems that know things to AI systems that do things. It’s no coincidence that technology companies then started to release their guides for building AI agents following MCP’s release – with Microsoft, Google, Cloudflare, OpenAI, and Amazon following close behind. Technology to watch Finally, a few key technologies that will define the next phase of AI include: Model Context Protocol (MCP) Continue to watch this standard. As more tools and platforms adopt MCP, the ecosystem of "plug-and-play" capabilities for agents will explode, as will the security risks. Simon Willison puts it perfectly by describing a “lethal trifecta”. AI systems with access to private data, the ability to communicate externally, and exposure to untrusted content could easily lead to serious consequences. Source: Simon Willison Authorisation for AI systems As agents move from knowing things to doing things (e.g., booking travel, purchasing supplies, modifying code), security becomes paramount. We need robust authorisation. This will involve human-in-the-loop (HITL) approvals, likely powered by modern authentication standards like Client-Initiated Backchannel Authentication (CIBA), which can send a push notification to a manager to approve an agent's action. Thought leaders from Microsoft suggest an overhaul to OAuth, with agentic systems having their own distinct identities and security considerations. One thing’s for sure: proper authorization is complex – difficult to get right and catastrophic to get wrong. Agent-to-agent communication Current AI agents are specialized for a specific purpose, but next-generation AI functionality comes through the use of multi-agent systems, which can be deployed in a variety of architectures, such as hierarchical or swarms. How agents communicate with each other, share memory, and share capabilities is still in its relative infancy, especially when AI agents may be hosted independently and written with different frameworks. Two competing protocols are emerging: Google's Agent2Agent protocol and IBM’s Agent Communication Protocol (ACP). It's too early to call a winner, but the development of a standard here will be a major milestone. We are at the beginning of the agentic era. 2025 is the year for experimentation. It's time to move from simply using AI to actively building with it, automating the tedious, and unlocking new forms of creativity and productivity. Getting the most out of AI If one thing’s for sure, it’s that the AI landscape is moving fast. So it’s crucial that you and your organisation are at the forefront of AI developments and making the most out of the latest technologies. Keep your eyes peeled for brand new labs in this space coming very soon! Our brand new collection will demystify terminology, explore the core concepts, and let you build and secure modern AI systems in a safe, sandbox environment. Sign up for email notifications from the Immersive Community so you don’t miss out on this brand new collection.Elevating Cyber Resilience: How GenAI is Revolutionizing Crisis Simulations
Cyber threats have become a pervasive force within the business world, elevating the need for regular cyber resilience exercises into an organization-wide imperative. Genuine resilience is about more than prevention. It’s the agility to identify, respond to, and recover seamlessly from disruptions, ensuring uninterrupted business operations. This approach, which acknowledges the inevitability of a cyber event, is the hallmark of truly resilient organizations. Crisis simulations and cyber exercises are core to cultivating this resilience. Traditional cyber exercises, often static and presentation-driven, tend to serve as theoretical validations. While valuable for reviewing playbooks and pinpointing theoretical vulnerabilities, they frequently fall short of genuinely testing incident response and crisis handling capabilities, particularly in the dynamic, high-pressure environment of a real-world attack. The sheer velocity of modern cyber threats, frequently powered by sophisticated AI, demands a new level of precision and relevance in simulations. This is where Generative AI (GenAI) comes in. It can transform how we design and execute tabletop-style cyber crisis simulations, making them profoundly relevant and impactful. The challenge of an unpredictable threat landscape While traditional crisis simulations are beneficial, they have certain limitations. The first is that it’s difficult and time-consuming to create realistic scenarios that reflect the latest threat actor tactics, techniques, and procedures (TTPs), and are meticulously tailored to an organization's unique infrastructure and risk profile. Analysts will also dedicate extensive hours to research, developing intricate narratives and manually injecting variables to ensure a robust challenge. However, this can sometimes result in a predictable exercise that doesn't fully prepare teams for the inherent chaos and unpredictability of a real-world incident. The human element in cyber resilience is also key. As Oliver Newbury, a member of Immersive's board of directors, recently emphasized: "Security is about people, process, and technology. I would have expected as much focus on upskilling people as on implementing new tools. It's the people using those tools who ultimately prevent breaches." Static simulations often fail to truly engage and challenge human teams, limiting their ability to build crucial muscle memory for swift decision-making under pressure. Elevating your crisis simulations with GenAI So, how does GenAI fit into the picture? This powerful technology can create novel content based on patterns learned from vast datasets. In doing so, it offers an unprecedented opportunity to inject realism and adaptability into crisis simulations. Just imagine the possibilities: Hyper-realistic scenario generation: GenAI can analyze current threat intelligence, recent attack patterns, and insights into your organization's specific weak spots to generate realistic and precisely tailored crisis scenarios. This ensures each exercise directly reflects the most pertinent and dangerous threats, making the experience far more impactful for your teams. Optimized playbook stress testing: GenAI doesn't just ease the exercise creation process – it can analyze your existing playbooks and processes. It can then generate crisis scenarios specifically designed to stress-test your response plans, ensuring they’re robust and effective under pressure. This helps validate that your playbooks and processes are truly ready for action. Realistic communications and media drills: In addition to the technical aspects, GenAI can simulate realistic internal and external communications during a crisis. It can generate mock press releases, social media posts, and even stakeholder questions, exercising your communications team's ability to manage public perception and share accurate information under pressure. This is critical for protecting your brand reputation during a breach. Instant feedback and analysis: After an exercise, GenAI can quickly crunch the data generated during the simulation, giving you detailed insights into team performance, response times, decision accuracy, and where you can improve. This speeds up the feedback loop, helping you tweak and strengthen your resilience strategies much faster. Tailored learning journeys: After an exercise, GenAI can analyze how an individual or team performed, then recommend follow-up scenarios or activities to address weaknesses or enhance key skills. This allows for truly personalized and continuously improving readiness programs. Think about the recent explosion of sophisticated, AI-driven attacks, from deepfake scams to highly targeted ransomware. Organizations have to be ready for these advanced threats, and old methods alone might not cut it. GenAI lets us simulate these next-gen attacks with a level of detail we couldn't even dream of before. This ensures teams aren’t just prepared for what's already happened – they’re ready for what's coming. Empowering your people It’s important to remember that GenAI is here to improve human expertise, not replace it. Just as information recall differs from true knowledge, GenAI is augmenting the critical "knowledge work" in cybersecurity, rather than replacing it. Our real value isn’t just in what we know, but how we apply, interpret, and synthesize that knowledge to drive meaningful outcomes. Our job is to use tools like GenAI to empower our organizations and teams and provide them with realistic and effective exercise environments. GenAI offloads the rote, time-consuming tasks of content creation and data sifting, freeing us up to focus on high-value actions such as analyzing results, mentoring teams, and fine-tuning strategic responses. This pushes us towards the "wisdom work" that truly defines expertise in cyber resilience. Building a culture of constant improvement The ultimate goal of bringing GenAI into crisis simulations is to build a culture of constant improvement, where cyber readiness isn’t just a checklist item, but a deep-seated organizational habit. By immersing our teams in hyper-relevant, dynamic, and challenging scenarios, we build the confidence, skills, and muscle memory they need to ride out the inevitable cyber storms with resilience and agility. How are you using GenAI to improve your cyber resilience? Share your thoughts and experiences in the comments below!
Your Guide to Effective AI Prompting
Why Prompting Matters for Crisis Simulations Think of AI as a highly intelligent, but literal, assistant. The quality of its output directly reflects the clarity and specificity of your instructions. For crisis simulations, this means: Relevance: Tailored scenarios that mirror your organization's unique risks, industry, and operational environment. Realism: Scenarios that feel authentic, with credible triggers, evolving complications, and realistic stakeholder reactions. Depth: Multi-layered scenarios that challenge your team's decision-making, communication, and collaboration skills. Actionability: Scenarios that provide clear learning objectives and reveal actionable insights for improving your crisis response plan. Core Principles of Effective Prompting Be Specific, Not Vague Bad Prompt "Generate a crisis." (Too generic, will give you a basic, unhelpful scenario.) Good Prompt "Generate a cybersecurity crisis scenario for a mid-sized e-commerce fashion retailer. The trigger is a ransomware attack that encrypts customer databases and disrupts order fulfillment." Why it works It defines the what (cybersecurity crisis, ransomware), the who (e-commerce fashion retailer, mid-sized), and the impact (encrypted databases, disrupted orders). Define your organisation and context using our drop down fields, and then add additional context. Industry (e.g., healthcare, finance, manufacturing, tech, retail) Threat (e.g., data breach, natural disaster, product recall, public relations nightmare, supply chain disruption, insider threat, workplace violence, financial fraud) Attack vector (e.g., phishing attack, severe weather event, manufacturing defect, viral social media post, disgruntled employee action, sudden market downturn) The more information the AI has about your specific context, the more tailored the scenario will be so consider adding further information such as: Company Size: (e.g., small startup, multinational corporation) Key Products/Services: (e.g., cloud-based software, physical goods, financial advisory) Target Audience: (e.g., B2B clients, general consumers, specific demographics) Geographic Scope: (e.g., local, national, global operations) Relevant Regulations/Compliance: (e.g., GDPR, HIPAA, industry-specific standards) Current Trends/Challenges: (e.g., supply chain issues, inflation, new technologies) Example: "Our company, 'Global Pharma Solutions,' is a multinational pharmaceutical company with a focus on novel drug development. We operate globally and are heavily regulated by the FDA and EMA. Generate a scenario reflecting a crisis involving a mislabeled drug batch, discovered shortly after market release in Europe and the US." Outline Key Stakeholders and Their Potential Reactions Realistic scenarios involve diverse stakeholders with varying interests and reactions. Internal: Employees, leadership, legal, HR, IT, communications, specific department teams. External: Customers, media, regulators, investors, suppliers, partners, general public, affected individuals. Desired Reaction: How should these stakeholders react? (e.g., panic, confusion, outrage, demanding answers, seeking legal action, offering support). Example: "Include reactions from panicked customers flooding social media, calls from concerned regulators, and an internal IT team struggling to diagnose the issue. Also, factor in a potential negative news story breaking on a major industry publication." Inject Complications and Escalation Crises rarely remain static. Build in elements that make the scenario evolve and become more challenging. Secondary Events: (e.g., power outage during a cyberattack, additional product defects discovered, key personnel unavailable) Information Gaps/Misinformation: (e.g., conflicting reports, rumors spreading on social media, difficulty in verifying facts) Ethical Dilemmas: (e.g., balancing transparency with legal implications, prioritizing different stakeholder needs) Time Constraints: (e.g., a critical decision needed within 30 minutes, public statement required by end of day) Example: "After the initial system outage, introduce a new complication: a cyber-espionage group claims responsibility on a dark web forum, threatening to release sensitive customer data if demands are not met, despite the initial incident being unrelated to a breach." Define the Learning Objectives (Optional, but Recommended) While the AI won't "know" your objectives, including them in your prompt can subtly guide its generation towards a scenario that helps you test specific aspects of your plan. Example: "The scenario should test our team's ability to communicate effectively under pressure," or "Focus on evaluating our supply chain resilience and alternative vendor protocols." By following these guidelines, you'll be well on your way to leveraging our AI crisis simulation feature to its fullest, preparing your team for any challenge the real world might throw at them. Happy simulating!From XSS and SQLi to AI-generated code and supply-chain compromise: How application security is evolving
Keeping up with vulnerabilities is like playing a never-ending game of whack-a-mole. One day, we were knee-deep in XSS payloads and buffer overflows; the next, developers everywhere were plugging SQL injection holes with duct tape and regex. A lot of our earlier tech wasn’t built with security in mind – or at least not at the forefront of our minds. But over the past two decades, the culture has changed, and developers are shifting left. Programming languages, as well as the frameworks and ecosystems around them, are evolving and adapting to evade security threats. XSS: From everyday headache to “mostly handled” Remember when cross-site scripting (XSS) was every web developer’s nightmare? In the 2000s, it seemed like every other website was vulnerable. If you were lucky, your users’ only punishment was an annoying pop-up. If not, credentials and session cookies were up for grabs. Modern languages and frameworks took proactive steps in their designs. Today, many of them have built-in protections against common vulnerabilities like XSS. Some examples are: React, Angular, Vue: Automatically escape output by default. You have to go out of your way to render raw HTML (and they make you feel guilty about it). Django, Ruby on Rails, ASP.NET Core: Templates escape user input by default. Browsers: Even they pitch in, with features like Content Security Policy (CSP). It’s still possible to write a vulnerable application, but the bar is higher. As a result, attackers have a bigger hurdle to jump over. This is thanks to the evolution of how languages and frameworks approach security – not as a feature, but as the default. SQL injection: OR 1=1 is mostly history SQL injection once powered major data breaches. Now, parameterized queries have become the norm, and we’re on our way to leaving those days behind. Object-Relational Mappers (ORMs) like SQLAlchemy, Entity Framework, and Hibernate generate safe SQL. Most modern languages make string concatenation in queries unnecessary (and uncool). Even PHP, once infamous for its “raw SQL everywhere” approach, now encourages prepared statements and offers safe database APIs. Not a perfect world, but much improved. It’s crucial to keep in mind, however, that technology shouldn’t be relied upon uncritically to keep applications secure. Developers must do their due diligence. Memory mischief: The rise of Rust (and memory-managed languages) C and C++ are legendary for performance. And legendary for buffer overflows, use-after-free, and all manner of memory mischief. Enter memory-safe languages: Java, C#, Python: Garbage collection and managed memory eliminate entire classes of bugs. Rust: Takes it up a notch with ownership semantics, preventing data races and dangling pointers at compile time. A lot of system-level work has migrated to these “safer” languages, and the impact is apparent in everything from embedded devices to operating systems (hello, Rust in the Linux kernel). The new frontiers: AI and supply chain attacks Just as one threat starts to become old news, new threats emerge. Two of these are: Supply chain compromise The SolarWinds breach and NPM “event-stream” incident have put supply chain attacks in the spotlight over the last decade. It goes to highlight that you can write perfect code and still get breached because a dependency many levels deep was compromised. Some of the changes we’re seeing as a result: Package registries are adopting MFA and signing requirements. Software Bill of Materials (SBOM) is becoming a must-have, especially in regulated industries. But the battle is ongoing. If anything, our code is more interconnected than ever, and the rise of “vibe coding” is complicating matters further. AI-generated code AI tools like GitHub Copilot and ChatGPT are generating millions of code snippets. This is a double-edged sword. You get increased productivity, but the code is often vulnerable. This is partly a reflection of the insecurities in the code that the models were trained on. I asked ChatGPT 4.5 to identify the vulnerability in the following code and it couldn't. Can you? Leave a reply with your thoughts! @limits(calls=6, period=60) @app.route("/change-password", methods=["POST"]) def change_password(): user = session.get("user") if not user: return jsonify({"message": "Unauthorised"}), 401 data = request.get_json() password = data.get("new_password") if not password: return jsonify({"message": "Password is required"}), 400 if not db.change_password(user, password): return jsonify({"message": "Failed to change password"}), 500 return jsonify({"message": "Password changed successfully"}), 200 @limits(calls=6, period=60) @app.route("/login", methods=["GET", "POST"]) def login(): data = request.get_json() user = data.get("user") password = data.get("password") if not user or not password: return jsonify({"message": "Username and password are required"}), 400 if not db.authenticate(user, password): return jsonify({"message": "Invalid username or password"}), 401 session["user"] = user return jsonify({"message": "Logged in successfully"}), 200 The changes I expect to see going forward are: Guidelines and governance for AI-generated code: Programming languages or coding standards may soon explicitly include guidelines, validation rules, or security frameworks tailored to AI-assisted coding, ensuring generated code adheres to secure patterns. Work is already being done to create rules files for improved security. Integrated security checks at the IDE level: IDEs may embed deeper vulnerability scanning and real-time feedback directly within coding processes. Development environments or compilers may also come integrated with validation tools specifically attuned to potential weaknesses inherent in AI-generated code. Increased reliance on static/dynamic security analysis tools: Enhanced automated scanning integrated into CI/CD pipelines, detecting flaws pre-deployment. Keeping people in the loop: Security awareness should be a top priority, so everyone is ready for the worst-case scenario. Proving and improving skills: Developers’ training should increasingly emphasize secure coding, particularly when assisted by AI tools. At the current stage, thorough PR reviews are more crucial than ever. As with any code, always review and test AI-generated code, especially for security-sensitive logic. AI is a tool, not an auditor. Security: Not a feature, but a default Application security has come a long way from the Wild West days of the early internet. The biggest shift in the software development landscape is that security is no longer a bolt-on. Modern programming languages and ecosystems try to make the secure path the easy path. Defaults are safe (escaped output, parameterized queries), dangerous operations are noisy (compiler warnings, explicit function names), and security updates are automated (thanks to package managers and CI/CD integrations). Of course, attackers are creative, and the landscape is always shifting. But the evolution of programming languages, as well as the surrounding tools and communities, means developers have a fighting chance. While we face newer threats like AI-generated code vulnerabilities and supply chain compromise, the foundations are getting stronger. The key is to keep learning, stay skeptical, and use the tools at your disposal properly. The biggest shift I would like to see is the human element no longer being viewed as the “weakest link”. The moles never stop popping up, but now, at least, we have better mallets – and the resources to help us use them.Deepfake Defense: Building a Cyber-Ready Workforce in the Age of GenAI
This event has now ended. Watch the recording here. GenAI is reshaping cybersecurity—enhancing defenses while also introducing new risks. Deepfakes, GenAI-generated malware, and vulnerabilities introduced through GenAI coding tools demand a strategic shift to stay ahead of evolving threats. Join industry experts Jamie Knobles and John Blythe as they examine real-world GenAI-driven attacks and share actionable insights to strengthen cyber resilience. This webinar will provide cyber leaders with: A deep-dive into deepfake tactics and other GenAI-related threats. Strategies to increase cyber resilience across the entire workforce. Recommendations for proving cyber capabilities. Register today to gain the knowledge and strategies you need to proactively address the GenAI challenge, minimizing risk and maximizing the potential of this transformative technology.Breaking Down Walls to Make Way for AI
The rapid rise in the popularity and application of AI has been unprecedented. We are actively experiencing the dawn of a revolutionary chapter in technology and innovation, but it also feels a little like this, don't you think? AI is everywhere. Even where you didn’t ask for it, or frankly may not want it. Its rise brings security risks that require comprehensive, strategic management. Are you training your teams on AI security risks? Are you reviewing procedures to protect your business as the threat surface expands? You’re not alone. Let’s get into how we’re guiding customers through these challenges. Immersive can provide knowledge, skills building, and engaging challenges for your teams to address risks from different angles. Artificial intelligence foundations To protect an organization, you need to know what you’re protecting from. These are some of our core recommendations: AI for Business Gain an understanding of various risks associated with implementing and integrating AI in a business context. These risks include understanding the challenges of implementation, potential issues in day-to-day AI utilization, and the broader implications of AI on operations. These labs will equip individuals with the knowledge to effectively leverage AI, while being mindful of these risks. AI Fundamentals Learn about emerging threats, generative AI models, and prompt injection attacks. Gain a comprehensive understanding of AI's implications in cybersecurity, AI-related security risks, and gain practical experience mitigating those risks. Build knowledge around internal risks AI often enters businesses through productivity tools or internal chatbots, like ChatGPT, for HR or finance queries. Using internal AI apps creates risks around data access, data handling, privilege management, improper use of LLM, and compliance considerations (or consequences). Some of these considerations are also relevant for external risks with AI. Here are some collections to get your team’s gears turning around AI-adjacent considerations and internal risk: Cloud Security Identity and Access Management Secrets and Encryption in AWS NIST – Guidelines on Security and Privacy in Public Cloud Computing (800-144) Risk and Compliance ISO 22381 – Security and Resilience for Identification Systems ISO 27014 – Governance of Information Security ISO 27018 – Protecting Private Data in Public Clouds ISO 28000 – Security Management Systems for Supply Chains ISO 31000 – Risk Management Let’s not forget – with new tools comes new access and alerting patterns. You’ll need to ensure your Digital Forensics and Incident Response (DFIR) teams are ready for new signal analysis and identifying corresponding indicators of compromise (IoCs) with new technologies. Digital Forensics Introduction to Digital Forensics Digital Forensics Threat Hunting – Theory Threat Hunting Introduction to Incident Response and Forensics in AWS Upskill to protect your business from external risk As customer-facing AI expands, so does your threat surface. The risks remain rooted in attacks humans conduct today; they’re just becoming more sophisticated with AI. If threat actors use AI maliciously against your business, you might see advanced social engineering attacks. This could include sophisticated phishing attacks or AI-generated voice, image, or video to manipulate users into disclosing credentials. Here are a few hands-on content recommendations that will keep your team response ready: Events and Breaches Gain familiarity with some of the biggest cyber events and most infamous data breaches in hacking history. Buckle up for interactive labs that will get you thinking about real-world events and how AI could affect these types of scenarios. Events and Breaches: Data Exposure Events and Breaches: Phishing Fraud Events and Breaches: Data Leaks Emerging Threats Attackers are quick to adopt new tools and tactics, giving them a first-mover advantage. Labs in this collection will get you hands-on with the latest methods used by threat actors around the globe. These labs aren’t explicitly focused on AI threats, but since AI threats are rooted in legacy techniques, this collection will help your team prepare for the variations AI may introduce. There are also increased risks with publicly facing AI tools that are integrated into internal databases or systems. These non-human identities have access to potentially sensitive databases, making them inherently open to prompt injection attacks in addition to legacy techniques. Here are some of our content recommendations to prepare your teams explicitly for these types of AI challenges: Fundamental AI Algorithms Gain a deep understanding of various AI algorithms and their practical applications in cybersecurity. Engage with labs on machine learning, deep learning, specific algorithms, and complete tasks such as implementing algorithms and analyzing results. Practitioners will gain hands-on experience in applying AI techniques to enhance cybersecurity measures and mitigate cyber threats. AI Challenges Test your knowledge and skills around AI security risks such as AI plugin injections, function calling, and prompt injection attacks. Complete hands-on exercises to find vulnerabilities in AI systems, beat the bot, and actively exploit vulnerable LLM implementations. Staying ahead in a rapidly evolving tech landscape requires continuous learning and skill-building. But readiness doesn’t just stop there. You must also be well-practiced in handling new and challenging situations. Regular exercises, like prompt injection attack detection and AI-driven social engineering tabletop drills, are essential for keeping your teams prepared. As threats evolve, Immersive will continue to deliver integrated labs and industry-leading exercising capabilities so your teams are ready to protect your business. Share your thoughts What skills are critical for your team to mitigate AI risks? Did you beat our AI Challenges? Are you hungry for another byte 👾? Comment below! Stay ready in the face of increased risks – bot or not. Get updates in your inbox on posts like this by following the Human Connection Blog!Mastering Crisis Sim: How We Got 25 Execs on the Edge of Their Seats
Let's start with the requirements. In this case, they were clear: 1) To have a fully customised exercise focusing on payment risks and cyber threats like fraud, ransomware, and cash-outs. This is the domain expertise of Mastercard, who had no issues creating this in the Immersive platform with our review and guidance. 2) To deliver this on-site in front of 25 senior execs (including the CEO, CTO, CISO, CFO, and numerous heads of departments). It’s definitely not easy getting this many board members together in one room, so we had to make the first best impression! What did we do, and why was it a success? The stakeholders told us that they’d run tabletop exercises before, so this wasn’t new to them. So, how did we turn what they expected to be a routine drill into an edge-of-the-seat experience? What made our approach unique? Realistic AI-generated videos The amazing Crisis Sim content creation team at Immersive Labs used generative AI to create highly realistic videos that felt as though they were straight from a Pakistani news channel. Mastercard provided us with a few scripts and ideas that helped bring the AI videos to life. AI video of a news reporter tailored to resemble the Pakistani news channel. AI video of a news reporter tailored to resemble the Pakistani news channel. Why did the execs jump out of their seats? That’s because we included the following in the videos: Footage of their building on the news and customer dissatisfaction over cyberattack repercussions. News reporters with local accents and dress styles mentioning the bank and stakeholders, and unhappy Tweets flooding social media. A video of their CEO, not his real face of course, but we used his name when it came to an inject where the stakeholders voted for ‘releasing a press statement to the public’. They even specifically asked us to ensure none of these videos leaked! But each video included the message “Exercise use only – not based on real events”. AI video of the CEO giving a press conference. Collaboration This success was a team effort. Getting any presentation done right, especially in person, requires planning and knowing your audience. This is where Mastercard brought their expertise to create and tailor the Crisis Sim exercise while we supported with our guidance and previous customer experience knowledge. Our roles, tasks, and responsibilities were clear. Top tip: If you’re planning an exercise, collaborate with others for ideas and support. Double act presentation Presenting as a team made all the difference. Mastercard’s expertise in engaging senior execs helped animate the session and avoid awkward silences, while I focused on facilitating the exercise. Doing this alone can be tricky – I’ve personally struggled with awkward silences during solo Crisis Sim sessions while juggling scenario questions, multiple responses, feedback, and facilitation all at once. Sharing the load was crucial, especially in a U-shaped room where I couldn’t easily move around. In this case, I didn’t need to worry about moving away from my laptop, leaving the close-up engagement completely for Mastercard to lead on. Image example of the U-shaped conference room this took place in. The feedback The feedback was overwhelmingly positive – they said it was their first experience with such a tabletop exercise, a stark contrast to their initial expectations. The AI videos sparked significant interest and curiosity. The collaboration between Mastercard and Immersive Labs successfully delivered an educational, engaging, and tailored exercise. The bank expressed interest in running a wider crisis drill in 2025 with multiple banks. So look forward to hearing about that in the future! Finding AI video scenarios in our Crisis Sim Catalogue If you clicked on any of the AI images above thinking it was a video, I’d like to apologise. They were just screenshots as the actual videos included sensitive, bank-specific details like names and locations that we can’t share externally. If you’d like to explore AI video scenarios in our Crisis Sim platform, check out titles like: Boardroom Betrayal: When Deepfakes Strike the Top Puppetmaster’s Revenge The Walls Have Ears – Part One The Walls Have Ears – Part Two
